Organizational Justice and Job Satisfaction Relation

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Introduction

This study aims to adopt a quantitative approach to test the relationship between organizational justice and turnover intention in the new generation employees in China. Organizational justice has received much attention from scholars over the last few decades. Such aspects as the impact of organizational justice on work-related outcomes, innovative performance, and organizational citizenship behavior became subject of research interest (Fang & Lim, 2002; Saw, 2005; Peterson, 2004). However, the connection between organizational justice and turnover intention concerning the new generation employees has not yet been established. This section includes information on the research design, sampling, data collection and measurement, procedure, data analysis plan, and research ethics.

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Research Design

This research is based on the idea that high quit intention in staff members can adversely affect a company. In particular, Duffield et al. (2011) list organizational instability, financial and human capital losses, and lower staff morale as possible outcomes of high turnover intention rates. Hence, there is a need to address the issue and investigate the role of organizational justice for the phenomenon at hand.

The current study was designed to explore the effect of three known forms of organizational justice on the turnover intention in generation Y employees. A quantitative method was applied; namely, an online survey was outlined and conducted to gather data for further analysis. For this study, a quantitative approach was chosen for the following reasons:

  • since a new area is being investigated, exploratory research is more suitable than descriptive one (Balnaves & Caputi, 2001);
  • this research aims not only to raise the issue that needs to be studied but also to determine the nature of the problem and the effects of organizational justice on employees’ turnover intention;
  • the quantitative approach is flexible and allows for adjustments to make data collection and analysis more efficient.

Moreover, the advantages and disadvantages of the chosen method were analyzed to justify its use. The merits of the quantitative approach include providing valuable insight into the issue, eliminating personal bias and prejudice, reaching a higher sample size, and quick data collection (Savela, 2018). The demerits refer to a somewhat limited understanding of the issue due to the methods’ classificatory nature and a hint of uncertainty regarding the results’ validity, which relies on respondents’ honesty (Slife & Melling, 2012). Overall, one can use the quantitative approach to understand employees’ turnover intention better, keeping its validity risks in mind.

Sampling

For this study, a simple random sampling method was employed as it allows for the representations and universality of the research. This study’s object is the new generation of employees, generation Y, and people born between 1977 and 1994 fall under this definition (Fernandez, 2009; Morton, 2002). Besides, the research aims to analyze the turnover intention of staff members in China. Hence, the sample is limited by demographics and geography, targeting Chinese employees from generation Y. Such a choice can be explained by the sample’s suitability to accomplish the research objectives and expand the knowledge on the connection between organizational justice and turnover intention.

A simple formula was used to calculate the sample size. This calculation aims to determine an optimal sample size to fulfill the study’s objectives (Naing et al., 2006). The confidence level accounts for 95% corresponding to the industry standards. The margin of error is 5%, and the expected prevalence is 10%. As per the formula provided by Naing et al. (2006), the sample size for the study should be around 138. Such a number allowed for an effective collection and an adequate assessment of data.

Data Collection / Measurement

Design of the Questionnaire

Given the research aim and sample size, the method of data collection decided upon was an online questionnaire. According to Saris & Gallhofer (2014), a questionnaire refers to questions with the choice of answers, explicitly designed for statistical study. It allows the researcher to obtain information for analysis quickly (Sekaran & Bougie, 2013). The data collection process for this study included two steps, such as the design and the conduction of the questionnaire.

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Research Variables

The questionnaire consists of two main parts: “Organizational Justice, Organizational Commitment and Turnover Intention Questionnaire” and “Personal Information.” For a better understanding of the design, it is crucial to determine the research variables first. As Kaur (2013) states, they “can be defined in terms of measurable factors through a process of operationalization.” (p. 36). The study is based on the assumption that a higher degree of organizational justice results in a lower turnover intention in employees (Cohen & Spector, 2001). It is pertinent to identify the variables and provide a more detailed insight into each one.

Independent Variable. Three known forms of organizational justice, including distributive justice, procedural justice, and interactional justice, refer to the independent variable. A three-item scale accepted by scholars and differentiating three organizational justice types was used in this study (Greenberg et al., 1993; Masterson et al., 2000). For distributional justice and procedural justice, a five-items scale introduced by Niehoff and Moorman (1993) was used, aiming to judge upon the extent of employees’ satisfaction with the organization’s allocation process and distribution result. For the dimension of interactional justice, Al-Zu’bi’s (2010) scale was adapted to measure the employee’s satisfaction with how justice is served in a company. As for the control condition in this study, a negative control group’s outcome will be examined to identify outside influences on the turnout intention.

Dependent Variable. The turnover intention in employees represents the dependent variable in the current research. For the measurement of turnover intention, the 22-item scale designed by Kelloway et al. (1999) was adapted. According to the conceptual framework by Chen et al. (2014), quit intention factors are interrelated. However, this study views the framework introduced by Zagladi et al. (2015) as more suitable. It allows for exploring the connection between main variables based on the research objectives.

Mediating Variable. Job burnout appears to have a mediating effect on the phenomena at hand. To measure the extent of workplace burnout, the Maslach Burnout Inventory (MBI) was applied (Maslach et al., 1996). The test enabled the research to single out the factors causing a rise in job burnout within a company. It is worth noting that the elements include but are not limited to emotional exhaustion and increased workload.

Moderating Variable. The moderating effect is attributed to power distance in the context of this study. High-power distance environments seem to lead to a higher level of employees’ acceptance of unfair treatment (Paine & Organ, 2000). To measure the sense of power distance, Hofstede’s Power distance Index was used (Shackleton & Ali, 1990). The moderating variable in this study appears to have a rather significant impact on the outcome.

Personal information. Personal information is represented by such elements as gender, age, education level, and seniority of the respondents. The purpose of collecting this type of data is explained by the need to portray the respondent. Since the study targets a particular group of people, it is critical to obtain demographical data to understand a broader picture of the issue and consider the influence of various factors.

The questionnaire adopted a five-point Likert scale of agreement to allow the respondents to express their consent or dissent with a particular statement, except for the part of personal information. By employing this method, one can gather systematic observations based on the data collected (O’Neill, 2017). The questionnaire suggests the following options for the respondents: from “1” to “5” respectively representing “very inconsistent”, “not very consistent”, “uncertain”, “basically consistent” and “very consistent”.

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Procedure

The procedure involved several steps, such as providing guidance on the survey for its participants, distributing questionnaires, collecting data, and analyzing data. First and foremost, an overall introduction of the survey, including the requirements and specifications of questionnaire filling, was ensured. An electronic version of the questionnaire was distributed to respondents as it allows for reaching a wider audience. The sample enterprises for data collection involved communication, finance, machinery manufacturing, and electric power and other fields, without narrowing the scope down to one particular occupation field. The findings of the study were analyzed and presented as per the data analysis plan.

Plan of Data Analysis

Upon the data collection, the questionnaires were analyzed by using SPSS 22.0 software. The purpose of the analysis was to study sample data characteristics to project it on the population characteristics (Verma, 2012). SPSS software allowed for processing the data by utilizing various statistical methods (Kremelberg, 2010). To test the reliability and validity, Cronbach’s alpha and Factor Analysis were applied separately (Basto & Pereira, 2012; Weaver & Maxwell, 2014). Later on, Pearson correlation analysis was carried out to examine the relationship between dependent and independent variables, with organization justice (distributive & procedural & interactional justice) as the independent variable (IV) and turnover intention as the dependent variable (DV) (Weaver & Koopman, 2014). Moreover, the regression analysis was conducted to check the moderating effect of power distance (Chatterjee & Hadi, 2015). The One-Way ANOVA method was applied regarding different employee characteristic variables. Therefore, the data analysis techniques helped to understand the meaning and potential of the analysis of the relationship between organizational justice and turnover intention.

Research Ethics

It is crucial to identify ethical concerns regarding research and sampling. Since the quantitative approach was applied, and personal data was obtained, consent from respondents was needed to collect, store, and secure the use of their data (Watley & May, 2004). Besides, the subjects were informed that the investigation was conducted anonymously and for academic purposes only. Hence, no privacy of enterprises and individuals was involved, and no threat of data misuse was presented.

References

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Balnaves, M., & Caputi, P. (2001). Introduction to quantitative research methods: An investigative approach. Sage.

Basto, M., & Pereira, J. M. (2012). An SPSS R-menu for ordinal factor analysis. Journal of statistical software, 46. Web.

Chatterjee, S., & Hadi, A. S. (2015). Regression analysis by example. John Wiley & Sons.

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Duffield, C., Roche, M., Blay, N., Thoms, D., & Stasa, H. (2011). The consequences of executive turnover. Journal of Research in Nursing, 16(6), 503–514. Web.

Fang, R., & Lim, V. K. G. (2002). Collectivism, machiavellianism, perceived organizational justice and organizational citizenship behavior: An empirical study of Chinese employees. International Conference on Electronic Business, 8. Web.

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Kaur, S. P. (2013). Variables in research. Indian Journal of Research and Reports in Medical Sciences, 3(4), 36-38. Web.

Kelloway, E. K., Gottlieb, B. H., & Barham, L. (1999). The source, nature, and direction of work and family conflict: A longitudinal investigation. Journal of Occupational Health Psychology, 4(4), 337–346. Web.

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Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). MBI: Maslach burnout inventory. CPP, Incorporated.

Masterson, S. S., Lewis, K., Goldman, B. M., & Taylor, M. S. (2000). Integrating justice and social exchange: The differing effects of fair procedures and treatment on work relationships. Academy of Management journal, 43(4), 738-748.

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Paine, J. B., & Organ, D. W. (2000). The cultural matrix of organizational citizenship behavior: Some preliminary conceptual and empirical observations. Human resource management review, 10(1), 45-59.

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Savela, T. (2018). The advantages and disadvantages of quantitative methods in schoolscape research. Linguistics and Education, 44, 31-44. Web.

Saw, W. K. (2005). Affect, organizational justice, and innovative performance: The mediating impact of leader-member exchange [Master’s Thesis, Universiti Sains Malaysia]. [email protected]

Sekaran, U., & Bougie, R. (2013) Research methods for business: A skill-building approach (6th ed.). Wiley.

Shackleton, V. J., & Ali, A. H. (1990). Work-related values of managers: A test of the Hofstede model. Journal of cross-cultural psychology, 21(1), 109-118. Web.

Slife, B. D., & Melling, B. S. (2012). Method decisions: The advantages and disadvantages of quantitative and quantitative modes of inquiry. Pastoral Psychology, 61(1), 1-28. Web.

Verma, J. P. (2012). Data analysis in management with SPSS software. Springer Science & Business Media.

Watley, L. D., & May, D. R. (2004). Enhancing moral intensity: The roles of personal and consequential information in ethical decision-making. Journal of Business Ethics, 50(2), 105-126.

Weaver, B., & Koopman, R. (2014). An SPSS macro to compute confidence intervals for Pearson’s correlation. The Quantitative Methods for Psychology, 10(1), 29-39.

Weaver, B., & Maxwell, H. (2014). Exploratory factor analysis and reliability analysis with missing data: A simple method for SPSS users. The Quantitative Methods for Psychology, 10(2), 143-152.

Zagladi, A. N., Hadiwidjojo, D., & Rahayu, M. (2015). The role of job satisfaction and power distance in determining the influence of organizational justice toward the turnover intention. Procedia-Social and Behavioral Sciences, 211, 42-48.

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